Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations64353
Missing cells1455
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.8 MiB
Average record size in memory144.0 B

Variable types

Text8
Categorical4
Numeric6

Alerts

Clean Alternative Fuel Vehicle (CAFV) Eligibility is highly overall correlated with Electric Range and 2 other fieldsHigh correlation
Electric Range is highly overall correlated with Clean Alternative Fuel Vehicle (CAFV) Eligibility and 1 other fieldsHigh correlation
Electric Vehicle Type is highly overall correlated with Clean Alternative Fuel Vehicle (CAFV) Eligibility and 2 other fieldsHigh correlation
Make is highly overall correlated with Clean Alternative Fuel Vehicle (CAFV) Eligibility and 1 other fieldsHigh correlation
State is highly overall correlated with ZIP CodeHigh correlation
ZIP Code is highly overall correlated with StateHigh correlation
State is highly imbalanced (99.3%) Imbalance
Electric Utility has 722 (1.1%) missing values Missing
ZIP Code is highly skewed (γ1 = -26.17372874) Skewed
ID has unique values Unique
DOL Vehicle ID has unique values Unique
Electric Range has 14938 (23.2%) zeros Zeros
Base MSRP has 61263 (95.2%) zeros Zeros

Reproduction

Analysis started2025-06-15 17:23:44.082259
Analysis finished2025-06-15 17:24:07.358256
Duration23.28 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ID
Text

Unique 

Distinct64353
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size502.9 KiB
2025-06-15T17:24:08.098639image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.8796482
Min length3

Characters and Unicode

Total characters442726
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64353 ?
Unique (%)100.0%

Sample

1st rowEV33174
2nd rowEV40247
3rd rowEV12248
4th rowEV55713
5th rowEV28799
ValueCountFrequency (%)
ev40247 1
 
< 0.1%
ev70672 1
 
< 0.1%
ev70740 1
 
< 0.1%
ev35919 1
 
< 0.1%
ev84495 1
 
< 0.1%
ev26346 1
 
< 0.1%
ev25141 1
 
< 0.1%
ev73428 1
 
< 0.1%
ev1574 1
 
< 0.1%
ev67089 1
 
< 0.1%
Other values (64343) 64343
> 99.9%
2025-06-15T17:24:09.200177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 64353
14.5%
V 64353
14.5%
1 33317
7.5%
4 32701
7.4%
2 32673
7.4%
8 32653
7.4%
3 32644
7.4%
7 32587
7.4%
5 32475
7.3%
6 32471
7.3%
Other values (2) 52499
11.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 442726
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 64353
14.5%
V 64353
14.5%
1 33317
7.5%
4 32701
7.4%
2 32673
7.4%
8 32653
7.4%
3 32644
7.4%
7 32587
7.4%
5 32475
7.3%
6 32471
7.3%
Other values (2) 52499
11.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 442726
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 64353
14.5%
V 64353
14.5%
1 33317
7.5%
4 32701
7.4%
2 32673
7.4%
8 32653
7.4%
3 32644
7.4%
7 32587
7.4%
5 32475
7.3%
6 32471
7.3%
Other values (2) 52499
11.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 442726
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 64353
14.5%
V 64353
14.5%
1 33317
7.5%
4 32701
7.4%
2 32673
7.4%
8 32653
7.4%
3 32644
7.4%
7 32587
7.4%
5 32475
7.3%
6 32471
7.3%
Other values (2) 52499
11.9%
Distinct5644
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size502.9 KiB
2025-06-15T17:24:09.741520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters643530
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1274 ?
Unique (%)2.0%

Sample

1st row5YJ3E1EC6L
2nd rowJN1AZ0CP8B
3rd rowWBY1Z2C56F
4th row1G1RD6E44D
5th row1G1FY6S05K
ValueCountFrequency (%)
5yjygdee9m 340
 
0.5%
5yjygdee0m 334
 
0.5%
5yjygdee8m 333
 
0.5%
5yjygdee6m 323
 
0.5%
5yjygdee2m 317
 
0.5%
5yjygdee4m 306
 
0.5%
5yjygdeexm 304
 
0.5%
5yjygdee7m 302
 
0.5%
5yjygdee1m 288
 
0.4%
5yjygdee5m 288
 
0.4%
Other values (5634) 61218
95.1%
2025-06-15T17:24:10.915979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 57081
 
8.9%
E 55788
 
8.7%
J 43480
 
6.8%
5 39535
 
6.1%
Y 38081
 
5.9%
A 32079
 
5.0%
3 28450
 
4.4%
C 26776
 
4.2%
D 22202
 
3.5%
G 21841
 
3.4%
Other values (23) 278217
43.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 643530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 57081
 
8.9%
E 55788
 
8.7%
J 43480
 
6.8%
5 39535
 
6.1%
Y 38081
 
5.9%
A 32079
 
5.0%
3 28450
 
4.4%
C 26776
 
4.2%
D 22202
 
3.5%
G 21841
 
3.4%
Other values (23) 278217
43.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 643530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 57081
 
8.9%
E 55788
 
8.7%
J 43480
 
6.8%
5 39535
 
6.1%
Y 38081
 
5.9%
A 32079
 
5.0%
3 28450
 
4.4%
C 26776
 
4.2%
D 22202
 
3.5%
G 21841
 
3.4%
Other values (23) 278217
43.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 643530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 57081
 
8.9%
E 55788
 
8.7%
J 43480
 
6.8%
5 39535
 
6.1%
Y 38081
 
5.9%
A 32079
 
5.0%
3 28450
 
4.4%
C 26776
 
4.2%
D 22202
 
3.5%
G 21841
 
3.4%
Other values (23) 278217
43.2%

County
Text

Distinct139
Distinct (%)0.2%
Missing4
Missing (%)< 0.1%
Memory size502.9 KiB
2025-06-15T17:24:11.239749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length20
Median length4
Mean length5.4533404
Min length4

Characters and Unicode

Total characters350917
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)0.1%

Sample

1st rowSnohomish
2nd rowSkagit
3rd rowPierce
4th rowKing
5th rowPierce
ValueCountFrequency (%)
king 33552
51.4%
snohomish 6920
 
10.6%
pierce 4825
 
7.4%
clark 3771
 
5.8%
thurston 2446
 
3.7%
kitsap 2302
 
3.5%
whatcom 1714
 
2.6%
spokane 1579
 
2.4%
benton 812
 
1.2%
skagit 779
 
1.2%
Other values (144) 6555
 
10.0%
2025-06-15T17:24:11.837552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 50732
14.5%
n 50003
14.2%
K 36167
10.3%
g 34582
9.9%
o 22058
 
6.3%
h 18517
 
5.3%
a 16780
 
4.8%
s 14197
 
4.0%
e 13869
 
4.0%
r 12670
 
3.6%
Other values (39) 81342
23.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 350917
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 50732
14.5%
n 50003
14.2%
K 36167
10.3%
g 34582
9.9%
o 22058
 
6.3%
h 18517
 
5.3%
a 16780
 
4.8%
s 14197
 
4.0%
e 13869
 
4.0%
r 12670
 
3.6%
Other values (39) 81342
23.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 350917
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 50732
14.5%
n 50003
14.2%
K 36167
10.3%
g 34582
9.9%
o 22058
 
6.3%
h 18517
 
5.3%
a 16780
 
4.8%
s 14197
 
4.0%
e 13869
 
4.0%
r 12670
 
3.6%
Other values (39) 81342
23.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 350917
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 50732
14.5%
n 50003
14.2%
K 36167
10.3%
g 34582
9.9%
o 22058
 
6.3%
h 18517
 
5.3%
a 16780
 
4.8%
s 14197
 
4.0%
e 13869
 
4.0%
r 12670
 
3.6%
Other values (39) 81342
23.2%

City
Text

Distinct544
Distinct (%)0.8%
Missing9
Missing (%)< 0.1%
Memory size502.9 KiB
2025-06-15T17:24:12.372294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length23
Mean length8.2481039
Min length3

Characters and Unicode

Total characters530716
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique174 ?
Unique (%)0.3%

Sample

1st rowLYNNWOOD
2nd rowBELLINGHAM
3rd rowTACOMA
4th rowREDMOND
5th rowPUYALLUP
ValueCountFrequency (%)
seattle 11887
 
15.9%
bellevue 3354
 
4.5%
redmond 2448
 
3.3%
vancouver 2303
 
3.1%
kirkland 2086
 
2.8%
island 2083
 
2.8%
sammamish 1874
 
2.5%
bothell 1771
 
2.4%
olympia 1550
 
2.1%
renton 1512
 
2.0%
Other values (574) 43719
58.6%
2025-06-15T17:24:13.225796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 76299
14.4%
A 53379
 
10.1%
L 50980
 
9.6%
T 40350
 
7.6%
N 35704
 
6.7%
O 33111
 
6.2%
S 32680
 
6.2%
R 27532
 
5.2%
I 23867
 
4.5%
M 21136
 
4.0%
Other values (17) 135678
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 530716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 76299
14.4%
A 53379
 
10.1%
L 50980
 
9.6%
T 40350
 
7.6%
N 35704
 
6.7%
O 33111
 
6.2%
S 32680
 
6.2%
R 27532
 
5.2%
I 23867
 
4.5%
M 21136
 
4.0%
Other values (17) 135678
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 530716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 76299
14.4%
A 53379
 
10.1%
L 50980
 
9.6%
T 40350
 
7.6%
N 35704
 
6.7%
O 33111
 
6.2%
S 32680
 
6.2%
R 27532
 
5.2%
I 23867
 
4.5%
M 21136
 
4.0%
Other values (17) 135678
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 530716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 76299
14.4%
A 53379
 
10.1%
L 50980
 
9.6%
T 40350
 
7.6%
N 35704
 
6.7%
O 33111
 
6.2%
S 32680
 
6.2%
R 27532
 
5.2%
I 23867
 
4.5%
M 21136
 
4.0%
Other values (17) 135678
25.6%

State
Categorical

High correlation  Imbalance 

Distinct38
Distinct (%)0.1%
Missing11
Missing (%)< 0.1%
Memory size502.9 KiB
WA
64168 
CA
 
40
MD
 
22
VA
 
21
TX
 
11
Other values (33)
 
80

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters128684
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowWA
2nd rowWA
3rd rowWA
4th rowWA
5th rowWA

Common Values

ValueCountFrequency (%)
WA 64168
99.7%
CA 40
 
0.1%
MD 22
 
< 0.1%
VA 21
 
< 0.1%
TX 11
 
< 0.1%
OR 6
 
< 0.1%
FL 5
 
< 0.1%
NV 5
 
< 0.1%
NC 5
 
< 0.1%
PA 4
 
< 0.1%
Other values (28) 55
 
0.1%
(Missing) 11
 
< 0.1%

Length

2025-06-15T17:24:13.474947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wa 64168
99.7%
ca 40
 
0.1%
md 22
 
< 0.1%
va 21
 
< 0.1%
tx 11
 
< 0.1%
or 6
 
< 0.1%
fl 5
 
< 0.1%
nv 5
 
< 0.1%
nc 5
 
< 0.1%
pa 4
 
< 0.1%
Other values (28) 55
 
0.1%

Most occurring characters

ValueCountFrequency (%)
A 64246
49.9%
W 64170
49.9%
C 51
 
< 0.1%
M 30
 
< 0.1%
D 27
 
< 0.1%
V 26
 
< 0.1%
N 25
 
< 0.1%
T 20
 
< 0.1%
I 12
 
< 0.1%
X 11
 
< 0.1%
Other values (14) 66
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 128684
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 64246
49.9%
W 64170
49.9%
C 51
 
< 0.1%
M 30
 
< 0.1%
D 27
 
< 0.1%
V 26
 
< 0.1%
N 25
 
< 0.1%
T 20
 
< 0.1%
I 12
 
< 0.1%
X 11
 
< 0.1%
Other values (14) 66
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 128684
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 64246
49.9%
W 64170
49.9%
C 51
 
< 0.1%
M 30
 
< 0.1%
D 27
 
< 0.1%
V 26
 
< 0.1%
N 25
 
< 0.1%
T 20
 
< 0.1%
I 12
 
< 0.1%
X 11
 
< 0.1%
Other values (14) 66
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 128684
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 64246
49.9%
W 64170
49.9%
C 51
 
< 0.1%
M 30
 
< 0.1%
D 27
 
< 0.1%
V 26
 
< 0.1%
N 25
 
< 0.1%
T 20
 
< 0.1%
I 12
 
< 0.1%
X 11
 
< 0.1%
Other values (14) 66
 
0.1%

ZIP Code
Real number (ℝ)

High correlation  Skewed 

Distinct678
Distinct (%)1.1%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean98143.453
Minimum745
Maximum99701
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size502.9 KiB
2025-06-15T17:24:13.793427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum745
5-th percentile98006
Q198052
median98121
Q398370
95-th percentile98928.8
Maximum99701
Range98956
Interquartile range (IQR)318

Descriptive statistics

Standard deviation2856.0643
Coefficient of variation (CV)0.029100915
Kurtosis713.79388
Mean98143.453
Median Absolute Deviation (MAD)100
Skewness-26.173729
Sum6.3152368 × 109
Variance8157103.5
MonotonicityNot monotonic
2025-06-15T17:24:14.165487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98052 1712
 
2.7%
98033 1223
 
1.9%
98004 1141
 
1.8%
98115 1105
 
1.7%
98012 1020
 
1.6%
98006 1007
 
1.6%
98040 975
 
1.5%
98074 933
 
1.4%
98103 897
 
1.4%
98034 862
 
1.3%
Other values (668) 53472
83.1%
ValueCountFrequency (%)
745 1
< 0.1%
2124 1
< 0.1%
2127 1
< 0.1%
2842 1
< 0.1%
6340 1
< 0.1%
6371 1
< 0.1%
6379 1
< 0.1%
7094 1
< 0.1%
7438 1
< 0.1%
9751 1
< 0.1%
ValueCountFrequency (%)
99701 1
 
< 0.1%
99567 1
 
< 0.1%
99403 25
 
< 0.1%
99402 6
 
< 0.1%
99362 136
0.2%
99361 5
 
< 0.1%
99360 4
 
< 0.1%
99357 7
 
< 0.1%
99356 2
 
< 0.1%
99354 126
0.2%

Model Year
Real number (ℝ)

Distinct19
Distinct (%)< 0.1%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2018.1862
Minimum1993
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size502.9 KiB
2025-06-15T17:24:14.445951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1993
5-th percentile2013
Q12017
median2018
Q32021
95-th percentile2022
Maximum2022
Range29
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7267417
Coefficient of variation (CV)0.0013510853
Kurtosis-0.046037675
Mean2018.1862
Median Absolute Deviation (MAD)2
Skewness-0.67777241
Sum1.2986221 × 108
Variance7.4351202
MonotonicityNot monotonic
2025-06-15T17:24:14.704812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2021 13175
20.5%
2018 9750
15.2%
2020 7504
11.7%
2019 7382
11.5%
2017 6841
10.6%
2016 4425
 
6.9%
2022 3911
 
6.1%
2015 3481
 
5.4%
2013 3354
 
5.2%
2014 2642
 
4.1%
Other values (9) 1881
 
2.9%
ValueCountFrequency (%)
1993 1
 
< 0.1%
1998 1
 
< 0.1%
1999 2
 
< 0.1%
2000 4
 
< 0.1%
2002 2
 
< 0.1%
2008 14
 
< 0.1%
2010 14
 
< 0.1%
2011 608
 
0.9%
2012 1235
 
1.9%
2013 3354
5.2%
ValueCountFrequency (%)
2022 3911
 
6.1%
2021 13175
20.5%
2020 7504
11.7%
2019 7382
11.5%
2018 9750
15.2%
2017 6841
10.6%
2016 4425
 
6.9%
2015 3481
 
5.4%
2014 2642
 
4.1%
2013 3354
 
5.2%

Make
Categorical

High correlation 

Distinct34
Distinct (%)0.1%
Missing4
Missing (%)< 0.1%
Memory size502.9 KiB
TESLA
27903 
NISSAN
8678 
CHEVROLET
6651 
FORD
3850 
KIA
3066 
Other values (29)
14201 

Length

Max length20
Median length14
Mean length5.5774604
Min length3

Characters and Unicode

Total characters358904
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowTESLA
2nd rowNISSAN
3rd rowBMW
4th rowCHEVROLET
5th rowCHEVROLET

Common Values

ValueCountFrequency (%)
TESLA 27903
43.4%
NISSAN 8678
 
13.5%
CHEVROLET 6651
 
10.3%
FORD 3850
 
6.0%
KIA 3066
 
4.8%
BMW 2707
 
4.2%
TOYOTA 2700
 
4.2%
AUDI 1265
 
2.0%
VOLKSWAGEN 1217
 
1.9%
CHRYSLER 1091
 
1.7%
Other values (24) 5221
 
8.1%

Length

2025-06-15T17:24:15.033718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tesla 27903
43.3%
nissan 8678
 
13.5%
chevrolet 6651
 
10.3%
ford 3850
 
6.0%
kia 3066
 
4.8%
bmw 2707
 
4.2%
toyota 2700
 
4.2%
audi 1265
 
2.0%
volkswagen 1217
 
1.9%
chrysler 1091
 
1.7%
Other values (29) 5254
 
8.2%

Most occurring characters

ValueCountFrequency (%)
S 49429
13.8%
A 47683
13.3%
E 46043
12.8%
T 41252
11.5%
L 38000
10.6%
N 20721
 
5.8%
O 19732
 
5.5%
I 16395
 
4.6%
R 14014
 
3.9%
H 9997
 
2.8%
Other values (17) 55638
15.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 358904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 49429
13.8%
A 47683
13.3%
E 46043
12.8%
T 41252
11.5%
L 38000
10.6%
N 20721
 
5.8%
O 19732
 
5.5%
I 16395
 
4.6%
R 14014
 
3.9%
H 9997
 
2.8%
Other values (17) 55638
15.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 358904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 49429
13.8%
A 47683
13.3%
E 46043
12.8%
T 41252
11.5%
L 38000
10.6%
N 20721
 
5.8%
O 19732
 
5.5%
I 16395
 
4.6%
R 14014
 
3.9%
H 9997
 
2.8%
Other values (17) 55638
15.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 358904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 49429
13.8%
A 47683
13.3%
E 46043
12.8%
T 41252
11.5%
L 38000
10.6%
N 20721
 
5.8%
O 19732
 
5.5%
I 16395
 
4.6%
R 14014
 
3.9%
H 9997
 
2.8%
Other values (17) 55638
15.5%

Model
Text

Distinct107
Distinct (%)0.2%
Missing13
Missing (%)< 0.1%
Memory size502.9 KiB
2025-06-15T17:24:15.661615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length24
Median length7
Mean length6.2701585
Min length2

Characters and Unicode

Total characters403422
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)< 0.1%

Sample

1st rowMODEL 3
2nd rowLEAF
3rd rowI3
4th rowVOLT
5th rowBOLT EV
ValueCountFrequency (%)
model 27870
27.7%
3 13138
13.1%
leaf 8679
 
8.6%
y 7622
 
7.6%
s 4710
 
4.7%
volt 3420
 
3.4%
ev 3221
 
3.2%
bolt 3042
 
3.0%
x 2400
 
2.4%
niro 2351
 
2.3%
Other values (97) 24165
24.0%
2025-06-15T17:24:16.528353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 47516
11.8%
E 47198
11.7%
O 42399
 
10.5%
36278
 
9.0%
M 31789
 
7.9%
D 29940
 
7.4%
A 18171
 
4.5%
3 15613
 
3.9%
I 15507
 
3.8%
F 12916
 
3.2%
Other values (28) 106095
26.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 403422
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 47516
11.8%
E 47198
11.7%
O 42399
 
10.5%
36278
 
9.0%
M 31789
 
7.9%
D 29940
 
7.4%
A 18171
 
4.5%
3 15613
 
3.9%
I 15507
 
3.8%
F 12916
 
3.2%
Other values (28) 106095
26.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 403422
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 47516
11.8%
E 47198
11.7%
O 42399
 
10.5%
36278
 
9.0%
M 31789
 
7.9%
D 29940
 
7.4%
A 18171
 
4.5%
3 15613
 
3.9%
I 15507
 
3.8%
F 12916
 
3.2%
Other values (28) 106095
26.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 403422
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 47516
11.8%
E 47198
11.7%
O 42399
 
10.5%
36278
 
9.0%
M 31789
 
7.9%
D 29940
 
7.4%
A 18171
 
4.5%
3 15613
 
3.9%
I 15507
 
3.8%
F 12916
 
3.2%
Other values (28) 106095
26.3%

Electric Vehicle Type
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size502.9 KiB
Battery Electric Vehicle (BEV)
47869 
Plug-in Hybrid Electric Vehicle (PHEV)
16484 

Length

Max length38
Median length30
Mean length32.049197
Min length30

Characters and Unicode

Total characters2062462
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBattery Electric Vehicle (BEV)
2nd rowBattery Electric Vehicle (BEV)
3rd rowBattery Electric Vehicle (BEV)
4th rowPlug-in Hybrid Electric Vehicle (PHEV)
5th rowBattery Electric Vehicle (BEV)

Common Values

ValueCountFrequency (%)
Battery Electric Vehicle (BEV) 47869
74.4%
Plug-in Hybrid Electric Vehicle (PHEV) 16484
 
25.6%

Length

2025-06-15T17:24:16.839205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-15T17:24:17.113688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
electric 64353
23.5%
vehicle 64353
23.5%
battery 47869
17.5%
bev 47869
17.5%
plug-in 16484
 
6.0%
hybrid 16484
 
6.0%
phev 16484
 
6.0%

Most occurring characters

ValueCountFrequency (%)
e 240928
11.7%
209543
10.2%
c 193059
9.4%
i 161674
 
7.8%
t 160091
 
7.8%
l 145190
 
7.0%
r 128706
 
6.2%
V 128706
 
6.2%
E 128706
 
6.2%
B 95738
 
4.6%
Other values (13) 470121
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2062462
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 240928
11.7%
209543
10.2%
c 193059
9.4%
i 161674
 
7.8%
t 160091
 
7.8%
l 145190
 
7.0%
r 128706
 
6.2%
V 128706
 
6.2%
E 128706
 
6.2%
B 95738
 
4.6%
Other values (13) 470121
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2062462
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 240928
11.7%
209543
10.2%
c 193059
9.4%
i 161674
 
7.8%
t 160091
 
7.8%
l 145190
 
7.0%
r 128706
 
6.2%
V 128706
 
6.2%
E 128706
 
6.2%
B 95738
 
4.6%
Other values (13) 470121
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2062462
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 240928
11.7%
209543
10.2%
c 193059
9.4%
i 161674
 
7.8%
t 160091
 
7.8%
l 145190
 
7.0%
r 128706
 
6.2%
V 128706
 
6.2%
E 128706
 
6.2%
B 95738
 
4.6%
Other values (13) 470121
22.8%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size502.9 KiB
Clean Alternative Fuel Vehicle Eligible
39948 
Eligibility unknown as battery range has not been researched
14938 
Not eligible due to low battery range
9467 

Length

Max length60
Median length39
Mean length43.580424
Min length37

Characters and Unicode

Total characters2804531
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClean Alternative Fuel Vehicle Eligible
2nd rowClean Alternative Fuel Vehicle Eligible
3rd rowClean Alternative Fuel Vehicle Eligible
4th rowClean Alternative Fuel Vehicle Eligible
5th rowClean Alternative Fuel Vehicle Eligible

Common Values

ValueCountFrequency (%)
Clean Alternative Fuel Vehicle Eligible 39948
62.1%
Eligibility unknown as battery range has not been researched 14938
 
23.2%
Not eligible due to low battery range 9467
 
14.7%

Length

2025-06-15T17:24:17.381766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-15T17:24:17.592714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
eligible 49415
12.3%
clean 39948
10.0%
alternative 39948
10.0%
fuel 39948
10.0%
vehicle 39948
10.0%
battery 24405
 
6.1%
range 24405
 
6.1%
not 24405
 
6.1%
eligibility 14938
 
3.7%
as 14938
 
3.7%
Other values (7) 88153
22.0%

Most occurring characters

ValueCountFrequency (%)
e 431537
15.4%
336098
12.0%
l 297965
10.6%
i 238478
 
8.5%
n 178991
 
6.4%
t 177516
 
6.3%
a 173520
 
6.2%
r 118634
 
4.2%
b 103696
 
3.7%
g 88758
 
3.2%
Other values (16) 659338
23.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2804531
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 431537
15.4%
336098
12.0%
l 297965
10.6%
i 238478
 
8.5%
n 178991
 
6.4%
t 177516
 
6.3%
a 173520
 
6.2%
r 118634
 
4.2%
b 103696
 
3.7%
g 88758
 
3.2%
Other values (16) 659338
23.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2804531
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 431537
15.4%
336098
12.0%
l 297965
10.6%
i 238478
 
8.5%
n 178991
 
6.4%
t 177516
 
6.3%
a 173520
 
6.2%
r 118634
 
4.2%
b 103696
 
3.7%
g 88758
 
3.2%
Other values (16) 659338
23.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2804531
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 431537
15.4%
336098
12.0%
l 297965
10.6%
i 238478
 
8.5%
n 178991
 
6.4%
t 177516
 
6.3%
a 173520
 
6.2%
r 118634
 
4.2%
b 103696
 
3.7%
g 88758
 
3.2%
Other values (16) 659338
23.5%

Electric Range
Real number (ℝ)

High correlation  Zeros 

Distinct98
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean106.94898
Minimum0
Maximum337
Zeros14938
Zeros (%)23.2%
Negative0
Negative (%)0.0%
Memory size502.9 KiB
2025-06-15T17:24:17.927405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114
median73
Q3215
95-th percentile291
Maximum337
Range337
Interquartile range (IQR)201

Descriptive statistics

Standard deviation104.09392
Coefficient of variation (CV)0.97330442
Kurtosis-1.2733495
Mean106.94898
Median Absolute Deviation (MAD)73
Skewness0.51100145
Sum6882488
Variance10835.544
MonotonicityNot monotonic
2025-06-15T17:24:18.322447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14938
23.2%
215 4353
 
6.8%
84 2840
 
4.4%
220 2831
 
4.4%
238 2545
 
4.0%
208 1841
 
2.9%
19 1810
 
2.8%
25 1801
 
2.8%
53 1698
 
2.6%
291 1668
 
2.6%
Other values (88) 28028
43.6%
ValueCountFrequency (%)
0 14938
23.2%
6 658
 
1.0%
8 25
 
< 0.1%
9 11
 
< 0.1%
10 97
 
0.2%
11 3
 
< 0.1%
12 100
 
0.2%
13 236
 
0.4%
14 795
 
1.2%
15 49
 
0.1%
ValueCountFrequency (%)
337 40
 
0.1%
330 213
 
0.3%
322 1091
1.7%
308 344
 
0.5%
293 299
 
0.5%
291 1668
2.6%
289 436
 
0.7%
270 168
 
0.3%
266 1041
1.6%
265 105
 
0.2%

Base MSRP
Real number (ℝ)

Zeros 

Distinct37
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2524.9908
Minimum0
Maximum845000
Zeros61263
Zeros (%)95.2%
Negative0
Negative (%)0.0%
Memory size502.9 KiB
2025-06-15T17:24:18.673514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum845000
Range845000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation12402.895
Coefficient of variation (CV)4.9120557
Kurtosis357.04375
Mean2524.9908
Median Absolute Deviation (MAD)0
Skewness9.5630932
Sum1.6249073 × 108
Variance1.5383181 × 108
MonotonicityNot monotonic
2025-06-15T17:24:18.995134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 61263
95.2%
69900 1062
 
1.7%
34600 359
 
0.6%
31950 305
 
0.5%
28500 148
 
0.2%
52900 143
 
0.2%
38500 127
 
0.2%
32250 113
 
0.2%
59900 105
 
0.2%
54950 101
 
0.2%
Other values (27) 627
 
1.0%
ValueCountFrequency (%)
0 61263
95.2%
28500 148
 
0.2%
31950 305
 
0.5%
32000 1
 
< 0.1%
32250 113
 
0.2%
32995 1
 
< 0.1%
33950 64
 
0.1%
34600 359
 
0.6%
34995 34
 
0.1%
35390 9
 
< 0.1%
ValueCountFrequency (%)
845000 1
 
< 0.1%
184400 9
< 0.1%
110950 14
< 0.1%
109000 6
< 0.1%
102000 11
< 0.1%
98950 14
< 0.1%
91250 1
 
< 0.1%
90700 12
< 0.1%
89100 5
 
< 0.1%
81100 11
< 0.1%

Legislative District
Real number (ℝ)

Distinct50
Distinct (%)0.1%
Missing169
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean29.951904
Minimum0
Maximum49
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size502.9 KiB
2025-06-15T17:24:19.366266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q119
median34
Q343
95-th percentile48
Maximum49
Range49
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.661124
Coefficient of variation (CV)0.48948887
Kurtosis-0.96278929
Mean29.951904
Median Absolute Deviation (MAD)11
Skewness-0.55273414
Sum1922433
Variance214.94855
MonotonicityNot monotonic
2025-06-15T17:24:19.780459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 4292
 
6.7%
45 4160
 
6.5%
48 3786
 
5.9%
36 3047
 
4.7%
46 2782
 
4.3%
43 2749
 
4.3%
1 2607
 
4.1%
5 2604
 
4.0%
37 2092
 
3.3%
34 2041
 
3.2%
Other values (40) 34024
52.9%
ValueCountFrequency (%)
0 6
 
< 0.1%
1 2607
4.1%
2 704
 
1.1%
3 340
 
0.5%
4 474
 
0.7%
5 2604
4.0%
6 580
 
0.9%
7 282
 
0.4%
8 697
 
1.1%
9 341
 
0.5%
ValueCountFrequency (%)
49 930
 
1.4%
48 3786
5.9%
47 976
 
1.5%
46 2782
4.3%
45 4160
6.5%
44 1468
 
2.3%
43 2749
4.3%
42 984
 
1.5%
41 4292
6.7%
40 1578
 
2.5%

DOL Vehicle ID
Real number (ℝ)

Unique 

Distinct64353
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9729049 × 108
Minimum4385
Maximum4.7893457 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size502.9 KiB
2025-06-15T17:24:20.165817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4385
5-th percentile9169979.2
Q11.3728649 × 108
median1.753776 × 108
Q32.2990389 × 108
95-th percentile4.753687 × 108
Maximum4.7893457 × 108
Range4.7893019 × 108
Interquartile range (IQR)92617406

Descriptive statistics

Standard deviation1.0694665 × 108
Coefficient of variation (CV)0.54207705
Kurtosis1.3295504
Mean1.9729049 × 108
Median Absolute Deviation (MAD)42287833
Skewness1.1076229
Sum1.2696235 × 1013
Variance1.1437585 × 1016
MonotonicityNot monotonic
2025-06-15T17:24:20.564006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
477551595 1
 
< 0.1%
3551852 1
 
< 0.1%
476091101 1
 
< 0.1%
128820107 1
 
< 0.1%
128670930 1
 
< 0.1%
475980130 1
 
< 0.1%
476344011 1
 
< 0.1%
119561984 1
 
< 0.1%
176233130 1
 
< 0.1%
169401634 1
 
< 0.1%
Other values (64343) 64343
> 99.9%
ValueCountFrequency (%)
4385 1
< 0.1%
4777 1
< 0.1%
10286 1
< 0.1%
10734 1
< 0.1%
12050 1
< 0.1%
23145 1
< 0.1%
27702 1
< 0.1%
35325 1
< 0.1%
46112 1
< 0.1%
61092 1
< 0.1%
ValueCountFrequency (%)
478934571 1
< 0.1%
478926346 1
< 0.1%
478925947 1
< 0.1%
478925163 1
< 0.1%
478924358 1
< 0.1%
478916028 1
< 0.1%
478910428 1
< 0.1%
478909938 1
< 0.1%
478909224 1
< 0.1%
478909070 1
< 0.1%
Distinct668
Distinct (%)1.0%
Missing510
Missing (%)0.8%
Memory size502.9 KiB
2025-06-15T17:24:21.258266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length29
Median length29
Mean length28.836599
Min length21

Characters and Unicode

Total characters1841015
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique207 ?
Unique (%)0.3%

Sample

1st rowPOINT (-122.287614 47.83874)
2nd rowPOINT (-122.414936 48.709388)
3rd rowPOINT (-122.396286 47.293138)
4th rowPOINT (-122.024951 47.670286)
5th rowPOINT (-122.321062 47.103797)
ValueCountFrequency (%)
point 63843
33.3%
122.122018 1712
 
0.9%
47.678465 1712
 
0.9%
122.188994 1223
 
0.6%
47.678406 1223
 
0.6%
122.132064 1201
 
0.6%
122.203169 1141
 
0.6%
47.619011 1141
 
0.6%
122.297534 1105
 
0.6%
47.685291 1105
 
0.6%
Other values (1326) 116123
60.6%
2025-06-15T17:24:22.184447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 191579
 
10.4%
1 154429
 
8.4%
4 148731
 
8.1%
127686
 
6.9%
. 127686
 
6.9%
7 119035
 
6.5%
6 89767
 
4.9%
3 83294
 
4.5%
8 76666
 
4.2%
5 76152
 
4.1%
Other values (10) 645990
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1841015
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 191579
 
10.4%
1 154429
 
8.4%
4 148731
 
8.1%
127686
 
6.9%
. 127686
 
6.9%
7 119035
 
6.5%
6 89767
 
4.9%
3 83294
 
4.5%
8 76666
 
4.2%
5 76152
 
4.1%
Other values (10) 645990
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1841015
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 191579
 
10.4%
1 154429
 
8.4%
4 148731
 
8.1%
127686
 
6.9%
. 127686
 
6.9%
7 119035
 
6.5%
6 89767
 
4.9%
3 83294
 
4.5%
8 76666
 
4.2%
5 76152
 
4.1%
Other values (10) 645990
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1841015
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 191579
 
10.4%
1 154429
 
8.4%
4 148731
 
8.1%
127686
 
6.9%
. 127686
 
6.9%
7 119035
 
6.5%
6 89767
 
4.9%
3 83294
 
4.5%
8 76666
 
4.2%
5 76152
 
4.1%
Other values (10) 645990
35.1%

Electric Utility
Text

Missing 

Distinct68
Distinct (%)0.1%
Missing722
Missing (%)1.1%
Memory size502.9 KiB
2025-06-15T17:24:22.490061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length112
Median length110
Mean length44.71121
Min length11

Characters and Unicode

Total characters2845019
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowPUGET SOUND ENERGY INC
2nd rowPUGET SOUND ENERGY INC
3rd rowBONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY
4th rowPUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)
5th rowBONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||ELMHURST MUTUAL POWER & LIGHT CO|PENINSULA LIGHT COMPANY
ValueCountFrequency (%)
of 61826
12.8%
57703
12.0%
wa 39461
 
8.2%
tacoma 38994
 
8.1%
sound 37342
 
7.7%
energy 37342
 
7.7%
puget 36953
 
7.7%
inc||city 22555
 
4.7%
power 14249
 
3.0%
inc 12879
 
2.7%
Other values (101) 122680
25.5%
2025-06-15T17:24:23.134499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
418353
14.7%
O 209923
 
7.4%
N 199850
 
7.0%
T 199682
 
7.0%
A 193964
 
6.8%
E 185047
 
6.5%
I 154348
 
5.4%
C 154027
 
5.4%
Y 104142
 
3.7%
U 97774
 
3.4%
Other values (26) 927909
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2845019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
418353
14.7%
O 209923
 
7.4%
N 199850
 
7.0%
T 199682
 
7.0%
A 193964
 
6.8%
E 185047
 
6.5%
I 154348
 
5.4%
C 154027
 
5.4%
Y 104142
 
3.7%
U 97774
 
3.4%
Other values (26) 927909
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2845019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
418353
14.7%
O 209923
 
7.4%
N 199850
 
7.0%
T 199682
 
7.0%
A 193964
 
6.8%
E 185047
 
6.5%
I 154348
 
5.4%
C 154027
 
5.4%
Y 104142
 
3.7%
U 97774
 
3.4%
Other values (26) 927909
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2845019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
418353
14.7%
O 209923
 
7.4%
N 199850
 
7.0%
T 199682
 
7.0%
A 193964
 
6.8%
E 185047
 
6.5%
I 154348
 
5.4%
C 154027
 
5.4%
Y 104142
 
3.7%
U 97774
 
3.4%
Other values (26) 927909
32.6%
Distinct210
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size502.9 KiB
2025-06-15T17:24:23.889544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.7491958
Min length1

Characters and Unicode

Total characters176919
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row50
2nd row15
3rd row18
4th row33.9
5th row41.78
ValueCountFrequency (%)
69 4816
 
7.5%
73 4767
 
7.4%
64 3632
 
5.6%
57 2791
 
4.3%
50 2524
 
3.9%
25 2033
 
3.2%
20 1952
 
3.0%
18 1944
 
3.0%
19 1754
 
2.7%
72 1668
 
2.6%
Other values (200) 36472
56.7%
2025-06-15T17:24:25.223612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 20248
11.4%
3 18458
10.4%
5 17363
9.8%
6 16801
9.5%
. 16773
9.5%
7 16612
9.4%
9 16431
9.3%
1 15838
9.0%
0 14486
8.2%
4 12809
7.2%
Other values (3) 11100
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 176919
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 20248
11.4%
3 18458
10.4%
5 17363
9.8%
6 16801
9.5%
. 16773
9.5%
7 16612
9.4%
9 16431
9.3%
1 15838
9.0%
0 14486
8.2%
4 12809
7.2%
Other values (3) 11100
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 176919
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 20248
11.4%
3 18458
10.4%
5 17363
9.8%
6 16801
9.5%
. 16773
9.5%
7 16612
9.4%
9 16431
9.3%
1 15838
9.0%
0 14486
8.2%
4 12809
7.2%
Other values (3) 11100
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 176919
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 20248
11.4%
3 18458
10.4%
5 17363
9.8%
6 16801
9.5%
. 16773
9.5%
7 16612
9.4%
9 16431
9.3%
1 15838
9.0%
0 14486
8.2%
4 12809
7.2%
Other values (3) 11100
6.3%

Interactions

2025-06-15T17:24:03.143797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:52.849906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:55.386869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:57.332049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:59.635967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:24:01.438283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:24:03.428752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:53.538869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:55.726987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:57.674792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:59.874806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:24:01.733193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:24:03.709994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:53.963385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:56.002728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:58.011438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:24:00.197272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:24:01.988894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:24:04.000466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:54.264589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:56.350146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:58.397234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:24:00.511028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:24:02.282784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:24:04.283263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:54.602079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:56.640569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:58.742148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:24:00.813299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:24:02.571001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:24:04.671984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:55.029338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:56.968033image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:23:59.244867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:24:01.155375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-06-15T17:24:02.851427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-06-15T17:24:25.434854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Base MSRPClean Alternative Fuel Vehicle (CAFV) EligibilityDOL Vehicle IDElectric RangeElectric Vehicle TypeLegislative DistrictMakeModel YearStateZIP Code
Base MSRP1.0000.0240.0260.0320.025-0.0050.256-0.1930.0230.004
Clean Alternative Fuel Vehicle (CAFV) Eligibility0.0241.0000.3610.6710.7270.0460.5920.4540.0020.007
DOL Vehicle ID0.0260.3611.000-0.0570.088-0.0030.097-0.1470.000-0.007
Electric Range0.0320.671-0.0571.0000.6340.0360.450-0.3140.009-0.051
Electric Vehicle Type0.0250.7270.0880.6341.0000.0950.7960.2060.0200.011
Legislative District-0.0050.046-0.0030.0360.0951.0000.0610.0280.023-0.365
Make0.2560.5920.0970.4500.7960.0611.0000.4080.0000.000
Model Year-0.1930.454-0.147-0.3140.2060.0280.4081.0000.000-0.071
State0.0230.0020.0000.0090.0200.0230.0000.0001.0000.973
ZIP Code0.0040.007-0.007-0.0510.011-0.3650.000-0.0710.9731.000

Missing values

2025-06-15T17:24:05.230606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-15T17:24:05.906120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-15T17:24:06.869724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDVIN (1-10)CountyCityStateZIP CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric UtilityExpected Price ($1k)
0EV331745YJ3E1EC6LSnohomishLYNNWOODWA98037.02020.0TESLAMODEL 3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible308032.0109821694POINT (-122.287614 47.83874)PUGET SOUND ENERGY INC50
1EV40247JN1AZ0CP8BSkagitBELLINGHAMWA98229.02011.0NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible73040.0137375528POINT (-122.414936 48.709388)PUGET SOUND ENERGY INC15
2EV12248WBY1Z2C56FPierceTACOMAWA98422.02015.0BMWI3Battery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible81027.0150627382POINT (-122.396286 47.293138)BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY18
3EV557131G1RD6E44DKingREDMONDWA98053.02013.0CHEVROLETVOLTPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible38045.0258766301POINT (-122.024951 47.670286)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)33.9
4EV287991G1FY6S05KPiercePUYALLUPWA98375.02019.0CHEVROLETBOLT EVBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible238025.0296998138POINT (-122.321062 47.103797)BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||ELMHURST MUTUAL POWER & LIGHT CO|PENINSULA LIGHT COMPANY41.78
5EV49859KMHE24L10GClarkVANCOUVERWA98683.02016.0HYUNDAISONATA PLUG-IN HYBRIDPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range273460018.0110121371POINT (-122.510748 45.603727)BONNEVILLE POWER ADMINISTRATION||PUD NO 1 OF CLARK COUNTY - (WA)16.365
6EV357781G1FZ6S07LKingSEATTLEWA98107.02020.0CHEVROLETBOLT EVBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible259036.0142015072NaNCITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)31.5
7EV531215YJSA1E22GSpokaneSPOKANEWA99224.02016.0TESLAMODEL SBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible21006.0349565044POINT (-117.505436 47.633834)MODERN ELECTRIC WATER COMPANY65
8EV468811N4BZ0CP9HKingBOTHELLWA98011.02017.0NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible10701.0327624048POINT (-122.197147 47.757791)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)19
9EV320041N4BZ0CP4GKingKENMOREWA98028.02016.0NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible84046.0148960990POINT (-122.246193 47.755504)PUGET SOUND ENERGY INC||CITY OF TACOMA - (WA)27
IDVIN (1-10)CountyCityStateZIP CodeModel YearMakeModelElectric Vehicle TypeClean Alternative Fuel Vehicle (CAFV) EligibilityElectric RangeBase MSRPLegislative DistrictDOL Vehicle IDVehicle LocationElectric UtilityExpected Price ($1k)
64343EV734282C4RC1S79NKitsapBAINBRIDGE ISLANDWA98110.02022.0CHRYSLERPACIFICAPlug-in Hybrid Electric Vehicle (PHEV)Clean Alternative Fuel Vehicle Eligible32023.0189998441POINT (-122.534497 47.643688)PUGET SOUND ENERGY INC38
64344EV15745YJYGDEF6MKingSHORELINEWA98177.02021.0TESLAMODEL YBattery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0032.0166439997POINT (-122.370159 47.743354)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)73
64345EV670895YJ3E1EA8NKingSEATTLEWA98144.02022.0TESLAMODEL 3Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0037.0183477598POINT (-122.30033 47.585339)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)64
64346EV24820YV4ED3UR6NKingSEATTLEWA98116.02022.0VOLVOXC40Battery Electric Vehicle (BEV)Eligibility unknown as battery range has not been researched0034.0186988966POINT (-122.394511 47.574001)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)95
64347EV461111N4AZ0CP9DKitsapPOULSBOWA98370.02013.0NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible75023.0263375976POINT (-122.633393 47.748427)PUGET SOUND ENERGY INC18
64348EV6357KNDCE3LG7LKingSEATTLEWA98144.02020.0KIANIROBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible239037.0156575107POINT (-122.30033 47.585339)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)33
64349EV423JTDKN3DP2DPierceTACOMAWA98402.02013.0TOYOTAPRIUS PLUG-INPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range6027.0211048701POINT (-122.443211 47.252172)BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PENINSULA LIGHT COMPANY13.3
64350EV278521G1FX6S05JKingSEATTLEWA98119.02018.0CHEVROLETBOLT EVBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible238036.0135543411POINT (-122.367721 47.639264)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)22.857
64351EV830WP1AE2A24HKingSEATTLEWA98115.02017.0PORSCHECAYENNEPlug-in Hybrid Electric Vehicle (PHEV)Not eligible due to low battery range14046.0192459907POINT (-122.297534 47.685291)CITY OF SEATTLE - (WA)|CITY OF TACOMA - (WA)45.7
64352EV111201N4BZ1CP8KLewisTOLEDOWA98591.02019.0NISSANLEAFBattery Electric Vehicle (BEV)Clean Alternative Fuel Vehicle Eligible150020.0477551595POINT (-122.800917 46.444012)BONNEVILLE POWER ADMINISTRATION||CITY OF TACOMA - (WA)||PUD NO 1 OF LEWIS COUNTY35